P. Polyakov

Publications (3)0 Total impact

• Conference Proceeding: Comparison of two neural networks approaches to Boolean matrix factorization

  P. Polyakov, A.A. Frolov, D. Husek
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  ABSTRACT: In this paper we compare two new neural networks methods, aimed at solving the problem of optimal binary matrix Boolean factorization or Boolean factor analysis. Neural network based Boolean factor analysis is a suitable method for a very large binary data sets mining including Web. Two types of neural networks based Boolean factor analyzers are analyzed. One based on feed forward neural network and second based on Hopfield-like recurrent neural network. We show that both methods give good results when processed data have a simple structure. But as the complexity of data structure grows, method based on feed forward neural network loses the ability to solve the Boolean factor analysis. In the method, based on the Hopfield like recurrent neural network, this effect is not observed.
  Networked Digital Technologies, 2009. NDT '09. First International Conference on; 08/2009
• Conference Proceeding: Clustering variables by classical approaches and neural network Boolean factor analysis

  A. Frolov, D. Husek, H. Rezankova, V. Snasel, P. Polyakov
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  ABSTRACT: In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis, hierarchical clustering, and a linear factor analysis on the mushroom dataset. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes.
  Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on; 07/2008
• Conference Proceeding: Image Analysis by Methods of Dimension Reduction

  P. Moravec, V. Snasel, A. Frolov, D. Husek, H. Rezankova, P. Polyakov
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  ABSTRACT: In this paper, we compare performance of several dimension reduction techniques, namely LSI, NMF, SDD, Boolean factor analysis, and cluster analysis. The qualitative comparison is evaluated on a collection of bars. We compare the quality of these methods from on the base of the visual impact.
  Computer Information Systems and Industrial Management Applications, 2007. CISIM '07. 6th International Conference on; 07/2007